Application of Excitation Moment for Enhancing Fault Diagnosis Probability of Rotating Blade

회전 블레이드의 결함진단 확률제고를 위한 가진 모멘트 적용

  • 김종수 (한양대학교 기계공학부) ;
  • 최찬규 (한양대학교 기계공학부) ;
  • 유홍희 (한양대학교 기계공학부)
  • Received : 2013.11.26
  • Accepted : 2013.12.12
  • Published : 2014.02.01


Recently, pattern recognition methods have been widely used by researchers for fault diagnoses of mechanical systems. A pattern recognition method determines the soundness of a mechanical system by detecting variations in the system's vibration characteristics. Hidden Markov models (HMMs) and artificial neural networks (ANNs) have recently been used as pattern recognition methods in various fields. In this study, a HMM-ANN hybrid method for the fault diagnosis of a mechanical system is introduced, and a rotating wind turbine blade with a crack is selected for fault diagnosis. The existence, location, and depth of said crack are identified in this research. For improving the diagnostic accuracy of the method in spite of the presence of noise, a moment with a few specific frequencies is applied to the structure.


Hidden Markov Model(HMM);Artificial Neural Network(ANN);Fault Diagnosis;Feature Vector;Vector Quantization


Supported by : 한국에너지기술평가원(KETEP)


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